Platform Recruitment
Machine Learning Engineer

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Job Title: Machine Learning Engineer, Physics AI
Location: London
Salary: Up to £100,000 DOE
A VC-backed AI startup is looking for a Machine Learning Engineer to help build foundational AI models that replace traditional physics simulation methods. The company is working on some of the hardest physical engineering challenges in automotive, aerospace, and energy, delivering AI that rivals simulation accuracy at orders of magnitude higher speed.
This is not a generic ML role. You will be working at the intersection of ML research and engineering, contributing to core model development, shaping architecture decisions, and deploying performant systems into real-world design optimisation workflows. You will work closely with ML researchers, software engineers, and industry partners on problems that have direct industrial and environmental impact.
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I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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What we are looking for
- MSc or PhD in Machine Learning, Computer Science, or a related quantitative field
- Strong track record applying ML to complex real-world problems, ideally involving geometry or physical systems
- Deep understanding of ML theory including optimisation, generalisation, and model architectures
- Strong Python skills with hands-on experience in PyTorch, TensorFlow, or JAX
- Experience deploying and monitoring models in production grade pipelines
- Ability to communicate complex ML concepts clearly to both technical and non-technical audiences
Particularly strong candidates will also have
- Familiarity with aerodynamic principles or computational fluid dynamics
- Experience with physics-informed machine learning or integrating physical constraints into models
- Experience with geometry representation for ML, including 3D or mesh-based approaches
- Prior experience with design optimisation algorithms in an engineering context


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Why this role
You will have direct influence over the architecture and direction of a platform redefining how physical engineering challenges are solved. The team includes veterans from world-leading AI labs and engineering firms, and the work has real-world impact on sustainable energy and efficient transport.
This role requires existing right to work in the UK. Visa sponsorship is not available.
If your background fits, apply below
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